A Quantitative Model for Natural Language Dataset with Evaluation Information:What Topic makes Consumer Reviews “Helpful”?

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  • 評価付き自然言語データの定量分析
  • 評価付き自然言語データの定量分析 : どのような消費者レビューが「参考になった」を集めるのか?
  • ヒョウカ ツキ シゼン ゲンゴ データ ノ テイリョウ ブンセキ : ドノ ヨウ ナ ショウヒシャ レビュー ガ 「 サンコウ ニ ナッタ 」 オ アツメル ノ カ?
  • —どのような消費者レビューが「参考になった」を集めるのか？—

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Abstract

<p> This study proposes LDA with evaluation information. The authors focus on online consumer reviews with “helpfulness of review”. The purpose is to find specific topics that affect consumer's decision making from many consumer reviews. The authors exploit an evaluation score in addition to review text which is described in the natural language for simultaneously classifying topic model. The “helpfulness” scores are accessed by readers and are with integers of zero or more. The authors apply the suggested model in reviews for coffeemakers crawled from consumer's review website. The results show that the model distinguishes two topic groups with extremity which indicates that is helpful or not. Also, it shows that the two topic groups also have qualitative differences among firms (brands). The authors discuss the application of the model and findings for both review website managers and firm's marketers. </p>

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